188 citations · 221 across the 12 of their papers we have counts for
5 papers · 1 filter
FLEDGE: Ledger-based Federated Learning Resilient to Inference and Backdoor Attacks
Jorge Castillo, Phillip Rieger, Hossein Fereidooni +2
Federated learning (FL) is a distributed learning process that uses a trusted aggregation server to allow multiple parties (or clients) to collaboratively train a machine learning…
FLAIRS: FPGA-Accelerated Inference-Resistant & Secure Federated Learning
Huimin Li, Phillip Rieger, Shaza Zeitouni +2
Federated Learning (FL) has become very popular since it enables clients to train a joint model collaboratively without sharing their private data. However, FL has been shown to be…
ARGUS: Context-Based Detection of Stealthy IoT Infiltration Attacks
Phillip Rieger, Marco Chilese, Reham Mohamed +3
IoT application domains, device diversity and connectivity are rapidly growing. IoT devices control various functions in smart homes and buildings, smart cities, and smart factorie…
AuthentiSense: A Scalable Behavioral Biometrics Authentication Scheme using Few-Shot Learning for Mobile Platforms
Hossein Fereidooni, Jan König, Phillip Rieger +5
Mobile applications are widely used for online services sharing a large amount of personal data online. One-time authentication techniques such as passwords and physiological biome…
BayBFed: Bayesian Backdoor Defense for Federated Learning
Kavita Kumari, Phillip Rieger, Hossein Fereidooni +2
Federated learning (FL) allows participants to jointly train a machine learning model without sharing their private data with others. However, FL is vulnerable to poisoning attacks…